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Sensors 2013, 13(9), 12536-12547; doi:10.3390/s130912536

Model-Based Spike Detection of Epileptic EEG Data

Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan
Medical Device Innovation Center, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan
Department of Neurology, National Cheng Kung University Hospital, No. 138, Sheng Li Road, Tainan City 704, Taiwan
Author to whom correspondence should be addressed.
Received: 17 June 2013 / Revised: 6 September 2013 / Accepted: 13 September 2013 / Published: 17 September 2013
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) is adopted to detect all possible spike candidates, then a newly proposed spike model with slow wave features is applied to these candidates for spike classification. Experimental results show that the proposed system, using the AdaBoost classifier, outperforms the conventional method in both two- and three-class EEG pattern classification problems. The proposed system not only achieves better accuracy for spike detection, but also provides new ability to differentiate between spikes and spikes with slow waves. Though spikes with slow waves occur frequently in epileptic EEGs, they are not used in conventional spike detection. Identifying spikes with slow waves allows the proposed system to have better capability for assisting clinical neurologists in routine EEG examinations and epileptic diagnosis. View Full-Text
Keywords: epilepsy; slow wave; spike detection; spike classification; nonlinear energy operator epilepsy; slow wave; spike detection; spike classification; nonlinear energy operator

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Liu, Y.-C.; Lin, C.-C.K.; Tsai, J.-J.; Sun, Y.-N. Model-Based Spike Detection of Epileptic EEG Data. Sensors 2013, 13, 12536-12547.

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